813 citations · 1.2k across the 10 of their papers we have counts for
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Which Training Methods for GANs do actually Converge?
Lars Mescheder, Andreas Geiger, Sebastian Nowozin
Recent work has shown local convergence of GAN training for absolutely continuous data and generator distributions. In this paper, we show that the requirement of absolute continui…
The Numerics of GANs
Lars Mescheder, Sebastian Nowozin, Andreas Geiger
In this paper, we analyze the numerics of common algorithms for training Generative Adversarial Networks (GANs). Using the formalism of smooth two-player games we analyze the assoc…
Adversarial Variational Bayes: Unifying Variational Autoencoders and Generative Adversarial Networks
Lars Mescheder, Sebastian Nowozin, Andreas Geiger
Variational Autoencoders (VAEs) are expressive latent variable models that can be used to learn complex probability distributions from training data. However, the quality of the re…
Probabilistic Duality for Parallel Gibbs Sampling without Graph Coloring
Lars Mescheder, Sebastian Nowozin, Andreas Geiger
We present a new notion of probabilistic duality for random variables involving mixture distributions. Using this notion, we show how to implement a highly-parallelizable Gibbs sam…